{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o_a38354lVVR"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "Videos are a sequence of images. Let's assume you have an image representation model (CNNs, ViTs, etc.) and a sequence model (RNNs, LSTMs, etc.) at hand. We ask you to tweak the models for video classification. The immediate thought would be to apply the image model to individual frames, then use the sequence model to learn the order of the image representation. Applying a classification head on the learned sequence representation completes the video classification model. [Video Classification with a CNN-RNN Architecture](https://keras.io/examples/vision/video_classification/) explains this approach in detail. Taking a step ahead, you can also build a hybrid Transformer-based model for video classification as shown in [Video Classification with Transformers](https://keras.io/examples/vision/video_transformers/).\n",
    "\n",
    "In this example, we minimally implement [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Arnab et al. The authors propose a **pure-transformer** based model for video classification. The authors propose a novel embedding scheme and many variants of Transformers to model on video clips. We implement the embedding scheme and one of the variants of the transformer architecture for simplicity.\n",
    "\n",
    "This example requires TensorFlow 2.6 or higher, and the medmnist python package can be installed by running the code cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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     "iopub.status.idle": "2024-03-04T07:37:34.682435Z",
     "shell.execute_reply": "2024-03-04T07:37:34.681863Z",
     "shell.execute_reply.started": "2024-03-04T07:37:24.529134Z"
    },
    "id": "Yo8dnWXhMZCY",
    "outputId": "087a3859-0db8-4bc1-e45b-89b489145c52",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
      "Collecting ipywidgets\n",
      "  Downloading https://mirrors.aliyun.com/pypi/packages/70/1a/7edeedb1c089d63ccd8bd5c0612334774e90cf9337de9fe6c82d90081791/ipywidgets-8.1.2-py3-none-any.whl (139 kB)\n",
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      "  Downloading https://mirrors.aliyun.com/pypi/packages/99/bc/82a8c3985209ca7c0a61b383c80e015fd92e74f8ba0ec1af98f9d6ca8dce/widgetsnbextension-4.0.10-py3-none-any.whl (2.3 MB)\n",
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      "\u001b[?25hCollecting jupyterlab-widgets~=3.0.10 (from ipywidgets)\n",
      "  Downloading https://mirrors.aliyun.com/pypi/packages/24/da/db1cb0387a7e4086780aff137987ee924e953d7f91b2a870f994b9b1eeb8/jupyterlab_widgets-3.0.10-py3-none-any.whl (215 kB)\n",
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      "Requirement already satisfied: executing>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n",
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      "Requirement already satisfied: six>=1.12.0 in /opt/conda/lib/python3.10/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n",
      "\u001b[33mDEPRECATION: pytorch-lightning 1.7.7 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
      "Successfully installed ipywidgets-8.1.2 jupyterlab-widgets-3.0.10 widgetsnbextension-4.0.10\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-03-04T07:15:08.382559Z",
     "iopub.status.busy": "2024-03-04T07:15:08.382287Z",
     "iopub.status.idle": "2024-03-04T07:15:12.355953Z",
     "shell.execute_reply": "2024-03-04T07:15:12.355360Z",
     "shell.execute_reply.started": "2024-03-04T07:15:08.382539Z"
    },
    "id": "XGIdspMdlVVS",
    "outputId": "05c4d584-5c11-48a1-c68e-084b6d4b817e",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -qq medmnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ALXGaR8lVVU"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
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    },
    "id": "3quv3egSlVVU",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-04 15:15:31.580097: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-03-04 15:15:31.608188: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-03-04 15:15:31.646421: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-03-04 15:15:31.646439: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-03-04 15:15:31.646458: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-03-04 15:15:31.655865: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-03-04 15:15:31.656565: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-03-04 15:15:32.672197: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import io\n",
    "import imageio\n",
    "import medmnist\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# setting seed for reproducibility\n",
    "SEED = 42\n",
    "os.environ[\"TF_CUDNN_DETERMINISTIC\"] = \"1\"\n",
    "keras.utils.set_random_seed(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EnmU6eWMlVVV"
   },
   "source": [
    "## Hyperparameters\n",
    "\n",
    "The hyperparameters are chosen specifically based on a hyperparameter search. You can find more on this in the Conclusion section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-04T07:15:36.484446Z",
     "iopub.status.busy": "2024-03-04T07:15:36.483975Z",
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     "shell.execute_reply": "2024-03-04T07:15:36.487491Z",
     "shell.execute_reply.started": "2024-03-04T07:15:36.484424Z"
    },
    "id": "_gghTdZslVVV",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# DATA\n",
    "DATASET_NAME = \"organmnist3d\"\n",
    "BATCH_SIZE = 32\n",
    "AUTO = tf.data.AUTOTUNE\n",
    "INPUT_SHAPE = (28, 28, 28, 1)\n",
    "NUM_CLASSES = 11\n",
    "\n",
    "# OPTIMIZER\n",
    "LEARNING_RATE = 1e-4\n",
    "WEIGHT_DECAY = 1e-5\n",
    "\n",
    "# TRAINING\n",
    "EPOCHS = 60\n",
    "\n",
    "# TUBELET EMBEDDING\n",
    "PATCH_SIZE = (8, 8, 8)\n",
    "NUM_PATCHES = (INPUT_SHAPE[0] // PATCH_SIZE[0]) ** 2\n",
    "\n",
    "# ViViT ARCHITECTURE\n",
    "LAYER_NORM_EPS = 1e-6\n",
    "PROJECTION_DIM = 128\n",
    "NUM_HEADS = 8\n",
    "NUM_LAYERS = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W9k0Fbp1lVVX"
   },
   "source": [
    "## Dataset\n",
    "\n",
    "For our example we use the [MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification](https://medmnist.com/) dataset. The videos are lightweight and easy to train on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2024-03-04T07:31:08.573746Z",
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     "shell.execute_reply.started": "2024-03-04T07:31:08.573722Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-04 15:31:08--  https://ccclouddisk.oss-cn-hangzhou.aliyuncs.com/organmnist3d.npz\n",
      "正在解析主机 ccclouddisk.oss-cn-hangzhou.aliyuncs.com (ccclouddisk.oss-cn-hangzhou.aliyuncs.com)... 118.31.219.201\n",
      "正在连接 ccclouddisk.oss-cn-hangzhou.aliyuncs.com (ccclouddisk.oss-cn-hangzhou.aliyuncs.com)|118.31.219.201|:443... 已连接。\n",
      "已发出 HTTP 请求，正在等待回应... 200 OK\n",
      "长度： 32657349 (31M) [application/octet-stream]\n",
      "正在保存至: ‘organmnist3d.npz’\n",
      "\n",
      "organmnist3d.npz    100%[===================>]  31.14M  10.8MB/s    用时 2.9s    \n",
      "\n",
      "2024-03-04 15:31:11 (10.8 MB/s) - 已保存 ‘organmnist3d.npz’ [32657349/32657349])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://modelscope.oss-cn-beijing.aliyuncs.com/resource/organmnist3d.npz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
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    },
    "id": "DF-8Gaz-lVVY",
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    "tags": []
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   "source": [
    "def download_and_prepare_dataset(data_info: dict):\n",
    "    \"\"\"\n",
    "    Utility function to download the dataset and return train/valid/test\n",
    "    videos and labels.\n",
    "    Arguments:\n",
    "        data_info (dict): Dataset metadata\n",
    "    \"\"\"\n",
    "    data_path = \"/mnt/workspace/organmnist3d.npz\"\n",
    "\n",
    "    with np.load(data_path) as data:\n",
    "        # Get videos\n",
    "        train_videos = data[\"train_images\"]\n",
    "        valid_videos = data[\"val_images\"]\n",
    "        test_videos = data[\"test_images\"]\n",
    "\n",
    "        # Get labels\n",
    "        train_labels = data[\"train_labels\"].flatten()\n",
    "        valid_labels = data[\"val_labels\"].flatten()\n",
    "        test_labels = data[\"test_labels\"].flatten()\n",
    "\n",
    "    return (\n",
    "        (train_videos, train_labels),\n",
    "        (valid_videos, valid_labels),\n",
    "        (test_videos, test_labels),\n",
    "    )\n",
    "\n",
    "\n",
    "# Get the metadata of the dataset\n",
    "info = medmnist.INFO[DATASET_NAME]\n",
    "\n",
    "# Get the dataset\n",
    "prepared_dataset = download_and_prepare_dataset(info)\n",
    "(train_videos, train_labels) = prepared_dataset[0]\n",
    "(valid_videos, valid_labels) = prepared_dataset[1]\n",
    "(test_videos, test_labels) = prepared_dataset[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p8MunQjflVVZ"
   },
   "source": [
    "### `tf.data` pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-04T07:31:42.120520Z",
     "iopub.status.busy": "2024-03-04T07:31:42.120217Z",
     "iopub.status.idle": "2024-03-04T07:31:42.400809Z",
     "shell.execute_reply": "2024-03-04T07:31:42.400100Z",
     "shell.execute_reply.started": "2024-03-04T07:31:42.120502Z"
    },
    "id": "nennp8VzlVVa",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-04 15:31:42.174862: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-03-04 15:31:42.247102: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2211] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    }
   ],
   "source": [
    "@tf.function\n",
    "def preprocess(frames: tf.Tensor, label: tf.Tensor):\n",
    "    \"\"\"Preprocess the frames tensors and parse the labels\"\"\"\n",
    "    # Preprocess images\n",
    "    frames = tf.image.convert_image_dtype(\n",
    "        frames[\n",
    "            ..., tf.newaxis\n",
    "        ],  # The new axis is to help for further processing with Conv3D layers\n",
    "        tf.float32,\n",
    "    )\n",
    "\n",
    "    # Parse label\n",
    "    label = tf.cast(label, tf.float32)\n",
    "    return frames, label\n",
    "\n",
    "\n",
    "def prepare_dataloader(\n",
    "    videos: np.ndarray,\n",
    "    labels: np.ndarray,\n",
    "    loader_type: str = \"train\",\n",
    "    batch_size: int = BATCH_SIZE,\n",
    "):\n",
    "    \"\"\"Utility function to prepare dataloader\"\"\"\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((videos, labels))\n",
    "\n",
    "    if loader_type == \"train\":\n",
    "        dataset = dataset.shuffle(BATCH_SIZE * 2)\n",
    "\n",
    "    dataloader = (\n",
    "        dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "        .batch(batch_size)\n",
    "        .prefetch(tf.data.AUTOTUNE)\n",
    "    )\n",
    "\n",
    "    return dataloader\n",
    "\n",
    "\n",
    "trainloader = prepare_dataloader(train_videos, train_labels, \"train\")\n",
    "validloader = prepare_dataloader(valid_videos, valid_labels, \"valid\")\n",
    "testloader = prepare_dataloader(test_videos, test_labels, \"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NZM6QmNSlVVb"
   },
   "source": [
    "## Tubelet Embedding\n",
    "\n",
    "In ViTs an image is divided into patches which is then spatially flattened and projected as a tokenization scheme. For a video one can repeat this process for individual frames. **Uniform frame sampling** as suggested by the authors is a tokenization scheme in which we sample frames from the video clip and perform simple ViT tokenization.\n",
    "\n",
    "| ![uniform frame sampling](https://i.imgur.com/aaPyLPX.png) |\n",
    "| :--: |\n",
    "| Uniform Frame Sampling [Source](https://arxiv.org/abs/2103.15691) |\n",
    "\n",
    "**Tubelet Embedding** is different in terms of capturing the temporal information. From the video we extract volumes. These volumes contain patches of the frame and the temporal information as well. The volumes are then flattened and projected to build video tokens.\n",
    "\n",
    "| ![tubelet embedding](https://i.imgur.com/9G7QTfV.png) |\n",
    "| :--: |\n",
    "| Tubelet Embedding [Source](https://arxiv.org/abs/2103.15691) |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-04T07:31:48.941137Z",
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    },
    "id": "nxvPq7L4lVVb",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TubeletEmbedding(layers.Layer):\n",
    "    def __init__(self, embed_dim, patch_size, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.projection = layers.Conv3D(\n",
    "            filters=embed_dim,\n",
    "            kernel_size=patch_size,\n",
    "            strides=patch_size,\n",
    "            padding=\"VALID\",\n",
    "        )\n",
    "        self.flatten = layers.Reshape(target_shape=(-1, embed_dim))\n",
    "\n",
    "    def call(self, videos):\n",
    "        projected_patches = self.projection(videos)\n",
    "        flattened_patches = self.flatten(projected_patches)\n",
    "        return flattened_patches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2YXp9X45lVVb"
   },
   "source": [
    "## Positional Embedding\n",
    "\n",
    "This layer adds positional information to encoded video tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-04T07:31:51.049269Z",
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    },
    "id": "IFM9wDOrlVVc",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class PositionalEncoder(layers.Layer):\n",
    "    def __init__(self, embed_dim, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.embed_dim = embed_dim\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        _, num_tokens, _ = input_shape\n",
    "        self.position_embedding = layers.Embedding(\n",
    "            input_dim=num_tokens, output_dim=self.embed_dim\n",
    "        )\n",
    "        self.positions = tf.range(start=0, limit=num_tokens, delta=1)\n",
    "\n",
    "    def call(self, encoded_tokens):\n",
    "        # Encode the positions and add it to the encoded tokens\n",
    "        encoded_positions = self.position_embedding(self.positions)\n",
    "        encoded_tokens = encoded_tokens + encoded_positions\n",
    "        return encoded_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rwRnXM4klVVc"
   },
   "source": [
    "## Video Vision Transformer\n",
    "\n",
    "The authors suggest 4 variants of Vision Transformer:\n",
    "\n",
    "- Spatio-temporal attention\n",
    "- Factorised encoder\n",
    "- Factorised self-attention\n",
    "- Factorised dot-product attention\n",
    "\n",
    "In this example, we will implement the **Spatio-temporal attention** model for simplicity. The following code snippet is heavily inspired from [Image classification with Vision Transformer](https://keras.io/examples/vision/image_classification_with_vision_transformer/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
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    },
    "id": "DFprppuNlVVc",
    "tags": []
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   "source": [
    "def create_vivit_classifier(\n",
    "    tubelet_embedder,\n",
    "    positional_encoder,\n",
    "    input_shape=INPUT_SHAPE,\n",
    "    transformer_layers=NUM_LAYERS,\n",
    "    num_heads=NUM_HEADS,\n",
    "    embed_dim=PROJECTION_DIM,\n",
    "    layer_norm_eps=LAYER_NORM_EPS,\n",
    "    num_classes=NUM_CLASSES,\n",
    "):\n",
    "\n",
    "    # Get the input layer\n",
    "    inputs = layers.Input(shape=input_shape)\n",
    "    # Create patches.\n",
    "    patches = tubelet_embedder(inputs)\n",
    "    # Encode patches.\n",
    "    encoded_patches = positional_encoder(patches)\n",
    "\n",
    "    # Create multiple layers of the Transformer block.\n",
    "    for _ in range(transformer_layers):\n",
    "        # Layer normalization and MHSA\n",
    "        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n",
    "        attention_output = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads, key_dim=embed_dim // num_heads, dropout=0.1\n",
    "        )(x1, x1)\n",
    "\n",
    "        # Skip connection\n",
    "        x2 = layers.Add()([attention_output, encoded_patches])\n",
    "\n",
    "        # Layer Normalization and MLP\n",
    "        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n",
    "        x3 = keras.Sequential(\n",
    "            [\n",
    "                layers.Dense(units=embed_dim * 4, activation=tf.nn.gelu),\n",
    "                layers.Dense(units=embed_dim, activation=tf.nn.gelu),\n",
    "            ]\n",
    "        )(x3)\n",
    "\n",
    "        # Skip connection\n",
    "        encoded_patches = layers.Add()([x3, x2])\n",
    "\n",
    "    # Layer normalization and Global average pooling.\n",
    "    representation = layers.LayerNormalization(epsilon=layer_norm_eps)(encoded_patches)\n",
    "    representation = layers.GlobalAvgPool1D()(representation)\n",
    "\n",
    "    # Classify outputs.\n",
    "    outputs = layers.Dense(units=num_classes, activation=\"softmax\")(representation)\n",
    "\n",
    "    # Create the Keras model.\n",
    "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IxENHgpflVVd"
   },
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-04T07:31:55.873591Z",
     "iopub.status.busy": "2024-03-04T07:31:55.873292Z",
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     "shell.execute_reply": "2024-03-04T07:31:55.877072Z",
     "shell.execute_reply.started": "2024-03-04T07:31:55.873573Z"
    },
    "id": "9rZTILtmlVVd",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def run_experiment():\n",
    "    # Initialize model\n",
    "    model = create_vivit_classifier(\n",
    "        tubelet_embedder=TubeletEmbedding(\n",
    "            embed_dim=PROJECTION_DIM, patch_size=PATCH_SIZE\n",
    "        ),\n",
    "        positional_encoder=PositionalEncoder(embed_dim=PROJECTION_DIM),\n",
    "    )\n",
    "\n",
    "    # Compile the model with the optimizer, loss function\n",
    "    # and the metrics.\n",
    "    optimizer = keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n",
    "    model.compile(\n",
    "        optimizer=optimizer,\n",
    "        loss=\"sparse_categorical_crossentropy\",\n",
    "        metrics=[\n",
    "            keras.metrics.SparseCategoricalAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.SparseTopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),\n",
    "        ],\n",
    "    )\n",
    "\n",
    "    # Train the model.\n",
    "    _ = model.fit(trainloader, epochs=EPOCHS, validation_data=validloader)\n",
    "\n",
    "    _, accuracy, top_5_accuracy = model.evaluate(testloader)\n",
    "    print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n",
    "    print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-03-04T07:31:58.962873Z",
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     "iopub.status.idle": "2024-03-04T07:35:19.173227Z",
     "shell.execute_reply": "2024-03-04T07:35:19.172698Z",
     "shell.execute_reply.started": "2024-03-04T07:31:58.962854Z"
    },
    "id": "2nf-iqdBlVVd",
    "outputId": "ea53cd9a-afd8-4622-a7c8-a928f6b38126",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "31/31 [==============================] - 13s 129ms/step - loss: 2.5460 - accuracy: 0.1112 - top-5-accuracy: 0.5541 - val_loss: 2.3522 - val_accuracy: 0.1677 - val_top-5-accuracy: 0.5839\n",
      "Epoch 2/60\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 2.2314 - accuracy: 0.1905 - top-5-accuracy: 0.6818 - val_loss: 2.0795 - val_accuracy: 0.1925 - val_top-5-accuracy: 0.7329\n",
      "Epoch 3/60\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 2.0678 - accuracy: 0.2266 - top-5-accuracy: 0.7724 - val_loss: 1.8490 - val_accuracy: 0.3540 - val_top-5-accuracy: 0.8137\n",
      "Epoch 4/60\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 1.9839 - accuracy: 0.2245 - top-5-accuracy: 0.7868 - val_loss: 1.7510 - val_accuracy: 0.3913 - val_top-5-accuracy: 0.8696\n",
      "Epoch 5/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 1.7727 - accuracy: 0.3296 - top-5-accuracy: 0.8713 - val_loss: 1.4922 - val_accuracy: 0.4348 - val_top-5-accuracy: 0.9130\n",
      "Epoch 6/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 1.5599 - accuracy: 0.4047 - top-5-accuracy: 0.8980 - val_loss: 1.4829 - val_accuracy: 0.4720 - val_top-5-accuracy: 0.9317\n",
      "Epoch 7/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 1.5090 - accuracy: 0.4336 - top-5-accuracy: 0.9320 - val_loss: 1.1957 - val_accuracy: 0.4845 - val_top-5-accuracy: 0.9752\n",
      "Epoch 8/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 1.3312 - accuracy: 0.4665 - top-5-accuracy: 0.9392 - val_loss: 1.1742 - val_accuracy: 0.5155 - val_top-5-accuracy: 0.9752\n",
      "Epoch 9/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 1.2566 - accuracy: 0.5129 - top-5-accuracy: 0.9516 - val_loss: 1.1218 - val_accuracy: 0.4845 - val_top-5-accuracy: 0.9876\n",
      "Epoch 10/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 1.1926 - accuracy: 0.5366 - top-5-accuracy: 0.9578 - val_loss: 1.0250 - val_accuracy: 0.6335 - val_top-5-accuracy: 0.9752\n",
      "Epoch 11/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 1.0615 - accuracy: 0.6087 - top-5-accuracy: 0.9660 - val_loss: 1.0074 - val_accuracy: 0.5714 - val_top-5-accuracy: 0.9565\n",
      "Epoch 12/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 1.0422 - accuracy: 0.5911 - top-5-accuracy: 0.9712 - val_loss: 0.8079 - val_accuracy: 0.6894 - val_top-5-accuracy: 0.9814\n",
      "Epoch 13/60\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.9497 - accuracy: 0.6395 - top-5-accuracy: 0.9763 - val_loss: 0.7175 - val_accuracy: 0.7391 - val_top-5-accuracy: 1.0000\n",
      "Epoch 14/60\n",
      "31/31 [==============================] - 3s 99ms/step - loss: 0.8286 - accuracy: 0.7199 - top-5-accuracy: 0.9856 - val_loss: 0.7042 - val_accuracy: 0.7640 - val_top-5-accuracy: 0.9876\n",
      "Epoch 15/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.7145 - accuracy: 0.7436 - top-5-accuracy: 0.9897 - val_loss: 0.4918 - val_accuracy: 0.8696 - val_top-5-accuracy: 0.9938\n",
      "Epoch 16/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.7636 - accuracy: 0.7240 - top-5-accuracy: 0.9835 - val_loss: 0.5838 - val_accuracy: 0.7950 - val_top-5-accuracy: 0.9876\n",
      "Epoch 17/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.7091 - accuracy: 0.7446 - top-5-accuracy: 0.9907 - val_loss: 0.6591 - val_accuracy: 0.7826 - val_top-5-accuracy: 0.9876\n",
      "Epoch 18/60\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.5728 - accuracy: 0.7858 - top-5-accuracy: 0.9928 - val_loss: 0.5149 - val_accuracy: 0.8012 - val_top-5-accuracy: 0.9938\n",
      "Epoch 19/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.6353 - accuracy: 0.7703 - top-5-accuracy: 0.9928 - val_loss: 0.6461 - val_accuracy: 0.7516 - val_top-5-accuracy: 0.9938\n",
      "Epoch 20/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.5357 - accuracy: 0.8187 - top-5-accuracy: 0.9887 - val_loss: 0.4122 - val_accuracy: 0.8509 - val_top-5-accuracy: 1.0000\n",
      "Epoch 21/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.5604 - accuracy: 0.8012 - top-5-accuracy: 0.9928 - val_loss: 0.3530 - val_accuracy: 0.9068 - val_top-5-accuracy: 1.0000\n",
      "Epoch 22/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.4070 - accuracy: 0.8455 - top-5-accuracy: 0.9959 - val_loss: 0.3766 - val_accuracy: 0.8882 - val_top-5-accuracy: 1.0000\n",
      "Epoch 23/60\n",
      "31/31 [==============================] - 3s 105ms/step - loss: 0.3584 - accuracy: 0.8744 - top-5-accuracy: 0.9969 - val_loss: 0.3561 - val_accuracy: 0.8696 - val_top-5-accuracy: 0.9938\n",
      "Epoch 24/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.4197 - accuracy: 0.8538 - top-5-accuracy: 0.9959 - val_loss: 0.4662 - val_accuracy: 0.8447 - val_top-5-accuracy: 0.9938\n",
      "Epoch 25/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.3401 - accuracy: 0.8795 - top-5-accuracy: 1.0000 - val_loss: 0.4369 - val_accuracy: 0.8571 - val_top-5-accuracy: 0.9938\n",
      "Epoch 26/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.3844 - accuracy: 0.8651 - top-5-accuracy: 1.0000 - val_loss: 0.4524 - val_accuracy: 0.8447 - val_top-5-accuracy: 0.9814\n",
      "Epoch 27/60\n",
      "31/31 [==============================] - 3s 99ms/step - loss: 0.3372 - accuracy: 0.8847 - top-5-accuracy: 0.9979 - val_loss: 0.3526 - val_accuracy: 0.8944 - val_top-5-accuracy: 0.9876\n",
      "Epoch 28/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.2570 - accuracy: 0.9022 - top-5-accuracy: 0.9990 - val_loss: 0.3503 - val_accuracy: 0.8882 - val_top-5-accuracy: 0.9938\n",
      "Epoch 29/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.2188 - accuracy: 0.9392 - top-5-accuracy: 0.9979 - val_loss: 0.2648 - val_accuracy: 0.9130 - val_top-5-accuracy: 1.0000\n",
      "Epoch 30/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.2039 - accuracy: 0.9331 - top-5-accuracy: 0.9990 - val_loss: 0.3587 - val_accuracy: 0.8696 - val_top-5-accuracy: 0.9938\n",
      "Epoch 31/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.1815 - accuracy: 0.9403 - top-5-accuracy: 1.0000 - val_loss: 0.3955 - val_accuracy: 0.8944 - val_top-5-accuracy: 0.9938\n",
      "Epoch 32/60\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.1658 - accuracy: 0.9434 - top-5-accuracy: 1.0000 - val_loss: 0.3539 - val_accuracy: 0.9068 - val_top-5-accuracy: 0.9876\n",
      "Epoch 33/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.1180 - accuracy: 0.9670 - top-5-accuracy: 1.0000 - val_loss: 0.3182 - val_accuracy: 0.9006 - val_top-5-accuracy: 0.9876\n",
      "Epoch 34/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0990 - accuracy: 0.9681 - top-5-accuracy: 1.0000 - val_loss: 0.3774 - val_accuracy: 0.8696 - val_top-5-accuracy: 1.0000\n",
      "Epoch 35/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0968 - accuracy: 0.9691 - top-5-accuracy: 1.0000 - val_loss: 0.4316 - val_accuracy: 0.8571 - val_top-5-accuracy: 1.0000\n",
      "Epoch 36/60\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.0905 - accuracy: 0.9701 - top-5-accuracy: 1.0000 - val_loss: 0.3164 - val_accuracy: 0.9130 - val_top-5-accuracy: 0.9938\n",
      "Epoch 37/60\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.0885 - accuracy: 0.9732 - top-5-accuracy: 1.0000 - val_loss: 0.4398 - val_accuracy: 0.8758 - val_top-5-accuracy: 0.9938\n",
      "Epoch 38/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.1502 - accuracy: 0.9495 - top-5-accuracy: 0.9990 - val_loss: 0.3972 - val_accuracy: 0.8882 - val_top-5-accuracy: 0.9938\n",
      "Epoch 39/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.1259 - accuracy: 0.9578 - top-5-accuracy: 1.0000 - val_loss: 0.3702 - val_accuracy: 0.9006 - val_top-5-accuracy: 0.9938\n",
      "Epoch 40/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0550 - accuracy: 0.9876 - top-5-accuracy: 1.0000 - val_loss: 0.4481 - val_accuracy: 0.8820 - val_top-5-accuracy: 0.9938\n",
      "Epoch 41/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0376 - accuracy: 0.9938 - top-5-accuracy: 1.0000 - val_loss: 0.4933 - val_accuracy: 0.8634 - val_top-5-accuracy: 0.9938\n",
      "Epoch 42/60\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.0370 - accuracy: 0.9928 - top-5-accuracy: 1.0000 - val_loss: 0.3740 - val_accuracy: 0.8944 - val_top-5-accuracy: 0.9876\n",
      "Epoch 43/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0175 - accuracy: 0.9990 - top-5-accuracy: 1.0000 - val_loss: 0.4246 - val_accuracy: 0.9006 - val_top-5-accuracy: 0.9876\n",
      "Epoch 44/60\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.0180 - accuracy: 0.9979 - top-5-accuracy: 1.0000 - val_loss: 0.4543 - val_accuracy: 0.8882 - val_top-5-accuracy: 0.9876\n",
      "Epoch 45/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0177 - accuracy: 0.9979 - top-5-accuracy: 1.0000 - val_loss: 0.5005 - val_accuracy: 0.8944 - val_top-5-accuracy: 0.9814\n",
      "Epoch 46/60\n",
      "31/31 [==============================] - 3s 99ms/step - loss: 0.0179 - accuracy: 0.9949 - top-5-accuracy: 1.0000 - val_loss: 0.3327 - val_accuracy: 0.9255 - val_top-5-accuracy: 0.9876\n",
      "Epoch 47/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0179 - accuracy: 0.9938 - top-5-accuracy: 1.0000 - val_loss: 0.4086 - val_accuracy: 0.8820 - val_top-5-accuracy: 0.9938\n",
      "Epoch 48/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0323 - accuracy: 0.9887 - top-5-accuracy: 1.0000 - val_loss: 0.4594 - val_accuracy: 0.8820 - val_top-5-accuracy: 0.9938\n",
      "Epoch 49/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0270 - accuracy: 0.9938 - top-5-accuracy: 1.0000 - val_loss: 0.4801 - val_accuracy: 0.9068 - val_top-5-accuracy: 0.9814\n",
      "Epoch 50/60\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.0622 - accuracy: 0.9794 - top-5-accuracy: 1.0000 - val_loss: 0.4554 - val_accuracy: 0.9193 - val_top-5-accuracy: 0.9876\n",
      "Epoch 51/60\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.1586 - accuracy: 0.9372 - top-5-accuracy: 0.9990 - val_loss: 0.6750 - val_accuracy: 0.8385 - val_top-5-accuracy: 0.9876\n",
      "Epoch 52/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.2376 - accuracy: 0.9186 - top-5-accuracy: 0.9990 - val_loss: 0.3382 - val_accuracy: 0.9068 - val_top-5-accuracy: 0.9876\n",
      "Epoch 53/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0962 - accuracy: 0.9743 - top-5-accuracy: 1.0000 - val_loss: 0.4793 - val_accuracy: 0.8758 - val_top-5-accuracy: 0.9752\n",
      "Epoch 54/60\n",
      "31/31 [==============================] - 3s 99ms/step - loss: 0.0536 - accuracy: 0.9815 - top-5-accuracy: 1.0000 - val_loss: 0.5233 - val_accuracy: 0.8509 - val_top-5-accuracy: 1.0000\n",
      "Epoch 55/60\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.0350 - accuracy: 0.9876 - top-5-accuracy: 1.0000 - val_loss: 0.4041 - val_accuracy: 0.9006 - val_top-5-accuracy: 0.9938\n",
      "Epoch 56/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0095 - accuracy: 0.9990 - top-5-accuracy: 1.0000 - val_loss: 0.3827 - val_accuracy: 0.9130 - val_top-5-accuracy: 1.0000\n",
      "Epoch 57/60\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0053 - accuracy: 1.0000 - top-5-accuracy: 1.0000 - val_loss: 0.3681 - val_accuracy: 0.9130 - val_top-5-accuracy: 1.0000\n",
      "Epoch 58/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0046 - accuracy: 1.0000 - top-5-accuracy: 1.0000 - val_loss: 0.3384 - val_accuracy: 0.9130 - val_top-5-accuracy: 1.0000\n",
      "Epoch 59/60\n",
      "31/31 [==============================] - 3s 99ms/step - loss: 0.0028 - accuracy: 1.0000 - top-5-accuracy: 1.0000 - val_loss: 0.3615 - val_accuracy: 0.9193 - val_top-5-accuracy: 1.0000\n",
      "Epoch 60/60\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0023 - accuracy: 1.0000 - top-5-accuracy: 1.0000 - val_loss: 0.3598 - val_accuracy: 0.9193 - val_top-5-accuracy: 1.0000\n",
      "20/20 [==============================] - 1s 33ms/step - loss: 1.0117 - accuracy: 0.7836 - top-5-accuracy: 0.9705\n",
      "Test accuracy: 78.36%\n",
      "Test top 5 accuracy: 97.05%\n"
     ]
    }
   ],
   "source": [
    "model = run_experiment()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PYEU8RiClVVd"
   },
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
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     ]
    },
    "execution": {
     "iopub.execute_input": "2024-03-04T07:37:54.031820Z",
     "iopub.status.busy": "2024-03-04T07:37:54.031432Z",
     "iopub.status.idle": "2024-03-04T07:37:55.468120Z",
     "shell.execute_reply": "2024-03-04T07:37:55.467692Z",
     "shell.execute_reply.started": "2024-03-04T07:37:54.031793Z"
    },
    "id": "pPHsf1t9lVVd",
    "outputId": "e5ae7012-702b-4106-fe47-39535f8d920d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
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      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72f303df20854dcd9389fa7ae4f64171",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "GridBox(children=(VBox(children=(HTML(value=\"'T: pancreas | P: pancreas'\"), Box(children=(Image(value=b'GIF89a…"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets\n",
    "NUM_SAMPLES_VIZ = 25\n",
    "testsamples, labels = next(iter(testloader))\n",
    "testsamples, labels = testsamples[:NUM_SAMPLES_VIZ], labels[:NUM_SAMPLES_VIZ]\n",
    "\n",
    "ground_truths = []\n",
    "preds = []\n",
    "videos = []\n",
    "\n",
    "\n",
    "for i, (testsample, label) in enumerate(zip(testsamples, labels)):\n",
    "    # Generate gif\n",
    "    with io.BytesIO() as gif:\n",
    "        imageio.mimsave(gif, (testsample.numpy() * 255).astype(\"uint8\")[..., 0], \"GIF\", fps=5)\n",
    "        videos.append(gif.getvalue())\n",
    "\n",
    "    # Get model prediction\n",
    "    output = model.predict(tf.expand_dims(testsample, axis=0))[0]\n",
    "    pred = np.argmax(output, axis=0)\n",
    "\n",
    "    ground_truths.append(label.numpy().astype(\"int\"))\n",
    "    preds.append(pred)\n",
    "\n",
    "\n",
    "def make_box_for_grid(image_widget, fit):\n",
    "    \"\"\"\n",
    "    Make a VBox to hold caption/image for demonstrating\n",
    "    option_fit values.\n",
    "    Source: https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Styling.html\n",
    "    \"\"\"\n",
    "    # Make the caption\n",
    "    if fit is not None:\n",
    "        fit_str = \"'{}'\".format(fit)\n",
    "    else:\n",
    "        fit_str = str(fit)\n",
    "\n",
    "    h = ipywidgets.HTML(value=\"\" + str(fit_str) + \"\")\n",
    "\n",
    "    # Make the green box with the image widget inside it\n",
    "    boxb = ipywidgets.widgets.Box()\n",
    "    boxb.children = [image_widget]\n",
    "\n",
    "    # Compose into a vertical box\n",
    "    vb = ipywidgets.widgets.VBox()\n",
    "    vb.layout.align_items = \"center\"\n",
    "    vb.children = [h, boxb]\n",
    "    return vb\n",
    "\n",
    "\n",
    "boxes = []\n",
    "for i in range(NUM_SAMPLES_VIZ):\n",
    "    ib = ipywidgets.widgets.Image(value=videos[i], width=100, height=100)\n",
    "    true_class = info[\"label\"][str(ground_truths[i])]\n",
    "    pred_class = info[\"label\"][str(preds[i])]\n",
    "    caption = f\"T: {true_class} | P: {pred_class}\"\n",
    "\n",
    "    boxes.append(make_box_for_grid(ib, caption))\n",
    "\n",
    "ipywidgets.widgets.GridBox(\n",
    "    boxes, layout=ipywidgets.widgets.Layout(grid_template_columns=\"repeat(5, 200px)\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C3Ij5fJJlVVf"
   },
   "source": [
    "## Final Thoughts\n",
    "\n",
    "With a vanilla implementation we achieve ~79-80% Top-1 accuracy on the test dataset.\n",
    "\n",
    "Places to improve:\n",
    "\n",
    "- Using data augmentation.\n",
    "- Using a better regularization scheme for training.\n",
    "- Apply different variants of the transformer model.\n",
    "\n",
    "The hyperparameters used in this tutorial were finalized by running a hyperparameter search using [W&B Sweeps](https://docs.wandb.ai/guides/sweeps). You can find out our sweeps result [here](https://wandb.ai/minimal-implementations/vivit/sweeps/66fp0lhz) and our quick analysis of the results [here](https://wandb.ai/minimal-implementations/vivit/reports/Hyperparameter-Tuning-Analysis--VmlldzoxNDEwNzcx).\n",
    "\n",
    "We are grateful to [Weights and Biases](https://wandb.ai/site) program for helping with GPU credits."
   ]
  }
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